SoroushVahidi/scidocs huggingface dataset:
Clustering intrusion detection alarms to support root cause analysis
(2003)

It is a well-known problem that intrusion detection systems overload their human operators by triggering thousands of alarms per day. This paper presents a new approach for handling intrusion detection alarms more efficiently. Central to this approach is the notion that each alarm occurs for a reason, which is referred to as the alarm's root causes. This paper observes that a few dozens of rather persistent root causes generally account for over 90% of the alarms that an intrusion detection system triggers. Therefore, we argue that alarms should be handled by identifying and removing the most predominant and persistent root causes. To make this paradigm practicable, we propose a novel alarm-clustering method that supports the human analyst in identifying root causes. We present experiments with real-world intrusion detection alarms to show how alarm clustering helped us identify root causes. Moreover, we show that the alarm load decreases quite substantially if the identified root causes are eliminated so that they can no longer trigger alarms in the future.
MieLog: A Highly Interactive Visual Log Browser Using Information Visualization and Statistical Analysis
(2002)

System administration has become an increasingly important function, with the fundamental task being the inspection of computer log-files. It is not, however, easy to perform such tasks for two reasons. One is the high recognition load of log contents due to the massive amount of textual data. It is a tedious, time-consuming and often error-prone task to read through them. The other problem is the difficulty in extracting unusual messages from the log. If an administrator does not have the knowledge or experience, he or she cannot readily recognize unusual log messages. To help address these issues, we have developed a highly interactive visual log browser called ‘‘MieLog.’’ MieLog uses two techniques for manual log inspection tasks: information visualization and statistical analysis. Information visualization is helpful in reducing the recognition load because it provides an alternative method of interpreting textual information without reading. Statistical analysis enables the extraction of unusual log messages without domain specific knowledge. We will give three examples that illustrate the ability of the MieLog system to isolate unusual messages more easily than before.
Bit Error Tolerance of a CIFAR-10 Binarized Convolutional Neural Network Processor
(2018)

Deployment of convolutional neural networks (ConvNets) in always-on Internet of Everything (IoE) edge devices is severely constrained by the high memory energy consumption of hardware ConvNet implementations. Leveraging the error resilience of ConvNets by accepting bit errors at reduced voltages presents a viable option for energy savings, but few implementations utilize this due to the limited quantitative understanding of how bit errors affect performance. This paper demonstrates the efficacy of SRAM voltage scaling in a 9-layer CIFAR-10 binarized ConvNet processor, achieving memory energy savings of 3.12× with minimal accuracy degradation (∼99% of nominal). Additionally, we quantify the effect of bit error accumulation in a multi-layer network and show that further energy savings are possible by splitting weight and activation voltages. Finally, we compare the measured error rates for the CIFAR-10 binarized ConvNet against MNIST networks to demonstrate the difference in bit error requirements across varying complexity in network topologies and classification tasks.
A Simple Model for Intrinsic Image Decomposition with Depth Cues
(2013)

We present a model for intrinsic decomposition of RGB-D images. Our approach analyzes a single RGB-D image and estimates albedo and shading fields that explain the input. To disambiguate the problem, our model estimates a number of components that jointly account for the reconstructed shading. By decomposing the shading field, we can build in assumptions about image formation that help distinguish reflectance variation from shading. These assumptions are expressed as simple nonlocal regularizers. We evaluate the model on real-world images and on a challenging synthetic dataset. The experimental results demonstrate that the presented approach outperforms prior models for intrinsic decomposition of RGB-D images.
Indirectly Encoding Neural Plasticity as a Pattern of Local Rules
(2010)

Biological brains can adapt and learn from past experience. In neuroevolution, i.e. evolving artificial neural networks (ANNs), one way that agents controlled by ANNs can evolve the ability to adapt is by encoding local learning rules. However, a significant problem with most such approaches is that local learning rules for every connection in the network must be discovered separately. This paper aims to show that learning rules can be effectively indirectly encoded by extending the Hypercube-based NeuroEvolution of Augmenting Topologies (HyperNEAT) method. Adaptive HyperNEAT is introduced to allow not only patterns of weights across the connectivity of an ANN to be generated by a function of its geometry, but also patterns of arbitrary learning rules. Several such adaptive models with different levels of generality are explored and compared. The long-term promise of the new approach is to evolve large-scale adaptive ANNs, which is a major goal for neuroevolution.
Breast cancer intrinsic subtype classification, clinical use and future trends.
(2015)

Breast cancer is composed of multiple subtypes with distinct morphologies and clinical implications. The advent of microarrays has led to a new paradigm in deciphering breast cancer heterogeneity, based on which the intrinsic subtyping system using prognostic multigene classifiers was developed. Subtypes identified using different gene panels, though overlap to a great extent, do not completely converge, and the avail of new information and perspectives has led to the emergence of novel subtypes, which complicate our understanding towards breast tumor heterogeneity. This review explores and summarizes the existing intrinsic subtypes, patient clinical features and management, commercial signature panels, as well as various information used for tumor classification. Two trends are pointed out in the end on breast cancer subtyping, i.e., either diverging to more refined groups or converging to the major subtypes. This review improves our understandings towards breast cancer intrinsic classification, current status on clinical application, and future trends.
A robust 2D point-sequence curve offset algorithm with multiple islands for contour-parallel tool path
(2013)

An offset algorithm is important to the contour-parallel tool path generation process. Usually, it is necessary to offset with islands. In this paper a new offset algorithm for a 2D point-sequence curve (PS-curve) with multiple islands is presented. The algorithm consists of three sub-processes, the islands bridging process, the raw offset curve generation and the global invalid loops removal. The input of the algorithm is a set of PS-curves, in which one of them is the outer profile and the others are islands. The bridging process bridges all the islands to the outer profile with the Delaunay triangulation method, forming a single linked PS-curve.With the fact that local problems are caused by intersections of adjacent bisectors, the concept of stuck circle is proposed. Based on stuck circle, local problems are fixed by updating the original profile with the proposed basic rule and append rule, so that a raw offset curve can be generated. The last process first reports all the self-intersections on the raw offset PS-curve, and then a procedure called tree analysis puts all the self-intersections into a tree. All the points between the nodes in even depth and its immediate children are collected using the collecting rule. The collected points form the valid loops, which is the output of the proposed algorithm. Each sub-process can be complete in near linear time, so the whole algorithm has a near linear time complexity. This can be proved by the examples tested in the paper. © 2012 Elsevier Ltd. All rights reserved.
Discovering facts with boolean tensor tucker decomposition
(2013)

Open Information Extraction (Open IE) has gained increasing research interest in recent years. The first step in Open IE is to extract raw subject--predicate--object triples from the data. These raw triples are rarely usable per se, and need additional post-processing. To that end, we proposed the use of Boolean Tucker tensor decomposition to simultaneously find the entity and relation synonyms and the facts connecting them from the raw triples. Our method represents the synonym sets and facts using (sparse) binary matrices and tensor that can be efficiently stored and manipulated. We consider the presentation of the problem as a Boolean tensor decomposition as one of this paper's main contributions. To study the validity of this approach, we use a recent algorithm for scalable Boolean Tucker decomposition. We validate the results with empirical evaluation on a new semi-synthetic data set, generated to faithfully reproduce real-world data features, as well as with real-world data from existing Open IE extractor. We show that our method obtains high precision while the low recall can easily be remedied by considering the original data together with the decomposition.
Syntax-Directed Variational Autoencoder for Structured Data
(2018)

Deep generative models have been enjoying success in modeling continuous data. However it remains challenging to capture the representations for discrete structures with formal grammars and semantics, e.g., computer programs and molecular structures. How to generate both syntactically and semantically correct data still remains largely an open problem. Inspired by the theory of compiler where the syntax and semantics check is done via syntax-directed translation (SDT), we propose a novel syntax-directed variational autoencoder (SD-VAE) by introducing stochastic lazy attributes. This approach converts the offline SDT check into on-the-fly generated guidance for constraining the decoder. Comparing to the state-of-the-art methods, our approach enforces constraints on the output space so that the output will be not only syntactically valid, but also semantically reasonable. We evaluate the proposed model with applications in programming language and molecules, including reconstruction and program/molecule optimization. The results demonstrate the effectiveness in incorporating syntactic and semantic constraints in discrete generative models, which is significantly better than current state-of-the-art approaches.
Color texture classification by integrative Co-occurrence matrices
(2004)

Integrative Co-occurrence matrices are introduced as novel features for color texture classi"cation. The extended Co-occurrence notation allows the comparison between integrative and parallel color texture concepts. The information pro"t of the new matrices is shown quantitatively using the Kolmogorov distance and by extensive classi"cation experiments on two datasets. Applying them to the RGB and the LUV color space the combined color and intensity textures are studied and the existence of intensity independent pure color patterns is demonstrated. The results are compared with two baselines: gray-scale texture analysis and color histogram analysis. The novel features improve the classi"cation results up to 20% and 32% for the "rst and second baseline, respectively. ? 2003 Pattern Recognition Society. Published by Elsevier Ltd. All rights reserved.
Design of hybrid resistive-capacitive DAC for SAR A/D converters
(2012)

While hybrid capacitive-resistive D/A Converter (DAC) has been known for many years, its potential for energy-efficient operation is sometimes overlooked. This paper investigates the utilization of hybrid DACs in successive-approximation register A/D converters. To improve energy efficiency of SAR ADCs, a new hybrid DAC is introduced. In an exemplar 10-bit 100-MS/s ADC, simulation results show that the energy efficiency and chip area (of passive devices) can be improved by more than an order of magnitude.
The Montreal Cognitive Assessment, MoCA: a brief screening tool for mild cognitive impairment.
(2005)

OBJECTIVES
To develop a 10-minute cognitive screening tool (Montreal Cognitive Assessment, MoCA) to assist first-line physicians in detection of mild cognitive impairment (MCI), a clinical state that often progresses to dementia.


DESIGN
Validation study.


SETTING
A community clinic and an academic center.


PARTICIPANTS
Ninety-four patients meeting MCI clinical criteria supported by psychometric measures, 93 patients with mild Alzheimer's disease (AD) (Mini-Mental State Examination (MMSE) score > or =17), and 90 healthy elderly controls (NC).


MEASUREMENTS
The MoCA and MMSE were administered to all participants, and sensitivity and specificity of both measures were assessed for detection of MCI and mild AD.


RESULTS
Using a cutoff score 26, the MMSE had a sensitivity of 18% to detect MCI, whereas the MoCA detected 90% of MCI subjects. In the mild AD group, the MMSE had a sensitivity of 78%, whereas the MoCA detected 100%. Specificity was excellent for both MMSE and MoCA (100% and 87%, respectively).


CONCLUSION
MCI as an entity is evolving and somewhat controversial. The MoCA is a brief cognitive screening tool with high sensitivity and specificity for detecting MCI as currently conceptualized in patients performing in the normal range on the MMSE.
Wind-Aware Emergency Landing Assistant Based on Dubins Curves
(2017)

A total engine failure poses a major threat to passengers as well as the aircraft and requires a fast decision by the pilot. We develop an assistant system to support the pilot in this decision process. An aircraft is able to glide a certain distance without thrust power by converting the potential energy of the altitude into distance. The objective of our work is to calculate an approach route which allows the aircraft to reach a suitable landing field at an appropriate altitude. This is a non-trivial problem because of many free parameters like wind direction and wind velocity. Our solution computes an approach route with two tangents and two co-rotating circular segments. For this purpose, valid approach routes can be calculated for many general cases. The method has a constant complexity and can dispense with iterative approaches. The route is calculated entirely in the wind frame which avoids complex calculations with trochoids. The central idea is to take the wind into account by moving the target towards the wind direction.
Dynamic Cascades for Face Detection
(2007)

In this paper, we propose a novel method, called "dynamic cascade", for training an efficient face detector on massive data sets. There are three key contributions. The first is a new cascade algorithm called "dynamic cascade ", which can train cascade classifiers on massive data sets and only requires a small number of training parameters. The second is the introduction of a new kind of weak classifier, called "Bayesian stump", for training boost classifiers. It produces more stable boost classifiers with fewer features. Moreover, we propose a strategy for using our dynamic cascade algorithm with multiple sets of features to further improve the detection performance without significant increase in the detector's computational cost. Experimental results show that all the new techniques effectively improve the detection performance. Finally, we provide the first large standard data set for face detection, so that future researches on the topic can be compared on the same training and testing set.
Providing Adaptive Courses in Learning Management Systems with Respect to Learning Styles
(2007)

Learning management systems (LMS) are commonly used in e-learning but provide little, or in most cases, no adaptivity. However, courses which adapt to the individual needs of students make learning easier for them and lead to a positive effect in learning. In this paper, we introduce a concept for providing adaptivity based on learning styles in LMS. In order to show the effectiveness of our approach, Moodle was extended by an add-on and an experiment with 437 students was performed. From the analysis of the students’ performance and behaviour in the course, we found out that students who learned from a course that matches their learning styles spent significantly less time in the course and achieved in average the same marks than students who got a course that either mismatched with their learning styles or included all available learning objects. Therefore, providing adaptive courses in LMS according to the proposed concept can be seen as effective in supporting students in learning.
A Tutorial on Logistic Regression
(1996)

Many procedures in SAs/STAT~ can be used to perform 10' gistic regression analysis: CATMOD, GENMOD,LOGISTIC, al)d PROBIT. Each procedure has special features that make. ~ useful for gertain applications. For most applica· tions, PROC LOGISTIC is the preferred choice,. It fits binary response or proportional odds models, provides various model·selection methods to identify important prognostic variables from a large number of candidate variables, and computes regression diagnostic statistics. This tutorial dis· cusses some of the problems users encountered when they used the LOGISTIC procedure.
Computational intelligence techniques for HVAC systems : A review
(2016)

Buildings are responsible for 40% of global energy use and contribute towards 30% of the total CO2 emissions. The drive to reduce energy use and associated greenhouse gas emissions from buildings has acted as a catalyst in the development of advanced computational methods for energy efficient design, management and control of buildings and systems. Heating, ventilation and air-conditioning (HVAC) systems are the major source of energy consumption in buildings and ideal candidates for substantial reductions in energy demand. Significant advances have been made in the past decades on the application of computational intelligence (CI) techniques for HVAC design, control, management, optimization, and fault detection and diagnosis. This article presents a comprehensive and critical review on the theory and applications of CI techniques for prediction, optimization, control and diagnosis of HVAC systems. The analysis of trends reveals that the minimisation of energy consumption was the key optimization objective in the reviewed research, closely followed by the optimization of thermal comfort, indoor air quality and occupant preferences. Hardcoded Matlab program was the most widely used simulation tool, followed by TRNSYS, EnergyPlus, DOE-2, HVACSim+ and ESP-r. Metaheuristic algorithms were the preferred CI method for solving HVAC related problems and in particular genetic algorithms were applied in most of the studies. Despite the low number of studies focussing on multi-agent systems (MAS), as compared to the other CI techniques, interest in the technique is increasing due to their ability of dividing and conquering an HVAC optimization problem with enhanced overall performance. The paper also identifies prospective future advancements and research directions.
Parallelization of DC Analysis through Multiport Decomposition
(2007)

Physical problems offer scope for macro level parallelization of solution by their essential structure. For parallelization of electrical network simulation, the most natural structure based method is that of multiport decomposition. In this paper this method is used for the simulation of electrical networks consisting of resistances, voltage and current sources using a distributed cluster of weakly coupled processors. At the two levels in which equations are solved in this method the authors have used sparse LU for both levels in the first scheme and sparse LU in the inner level and conjugate gradient in the outer level in the second scheme. Results are presented for planar networks, for the cases where the numbers of slave processors are 1 and 2, and for circuit sizes up to 8.2 million nodes and 16.4 million edges using 8 slave processors. The authors use a cluster of Pentium IV processors linked through a 10/100MBPS Ethernet switch
Online Learning with Predictable Sequences
(2013)

We present methods for online linear optimization that take advantage of benign (as opposed to worst-case) sequences. Specifically if the sequence encountered by the learner is described well by a known “predictable process”, the algorithms presented enjoy tighter bounds as compared to the typical worst case bounds. Additionally, the methods achieve the usual worst-case regret bounds if the sequence is not benign. Our approach can be seen as a way of adding prior knowledge about the sequence within the paradigm of online learning. The setting is shown to encompass partial and side information. Variance and path-length bounds [11, 9] can be seen as particular examples of online learning with simple predictable sequences. We further extend our methods and results to include competing with a set of possible predictable processes (models), that is “learning” the predictable process itself concurrently with using it to obtain better regret guarantees. We show that such model selection is possible under various assumptions on the available feedback. Our results suggest a promising direction of further research with potential applications to stock market and time series prediction.
Histogram-based image hashing scheme robust against geometric deformations
(2007)

In this paper, we propose a robust image hash algorithm by using the invariance of the image histogram shape to geometric deformations. Robustness and uniqueness of the proposed hash function are investigated in detail by representing the histogram shape as the relative relations in the number of pixels among groups of two different bins. It is found from extensive testing that the histogram-based hash function has a satisfactory performance to various geometric deformations, and is also robust to most common signal processing operations thanks to the use of Gaussian kernel low-pass filter in the preprocessing phase.